Talk by Ilya Sutskever from Google. Given to the Redwood Center for Theoretical Neuroscience at UC Berkeley.
Dictionaries and phrase tables are the basis of modern statistical machine translation systems. I will present a method that can automate the process of generating and extending dictionaries and phrase tables. Our method can translate missing word and phrase entries by learning language structures using large monolingual data, and by mapping between the languages using a small bilingual dataset. It uses distributed representations of words and learns a linear mapping between vector spaces of languages. Despite its simplicity, our method is surprisingly effective: we can achieve almost 90% precision@5 for translation of words between English and Spanish. This method makes little assumption about the languages, so it can be used to extend and refine dictionaries and translation tables for any language pairs. Joint work with Tomas Mikolov and Quoc Le.